Multi-step carbon emissions forecasting model for industrial process based on a new strategy and machine learning methods

Yusha Hu, Yi Man, Jingzheng Ren, Jianzhao Zhou, Zhiqiang Zeng (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The rising industrial sector capacity increases carbon emissions, necessitating a low-carbon transformation for global sustainability. Accurate multi-step forecasting of industrial carbon emissions is crucial for optimizing production, reducing energy consumption, improving efficiency, and achieving decarbonization. However, current forecasting faces challenges like model uncertainty and low accuracy with multi forecasting steps. To address these issues, this study proposed a multi-step carbon emissions forecasting model for industrial processes. Firstly, a new multi-step forecasting strategy framework was proposed. Secondly, SHapley Additive exPlanations (SHAP) and lagged autocorrelation analysis were used to extract key features affecting industrial carbon emissions. On this basis, the grey model, autoregressive moving average (ARMA) model, and least squares support vector machine (LSSVM) method were used to build forecasting models separately. The weights of the three models were solved using the induced ordered weighted average operator and the Monte Carlo search tree (MCST) method. The final forecasting results were obtained by using the weighted summation method. The data collected from the real industry were used to validate the proposed model. The results showed that the proposed model could accurately forecast industrial carbon emissions within 12 steps and its MAPE is less than 10%. The proposed multi-step industrial carbon emissions forecasting model can provide data support for industrial energy saving, carbon reduction, and green and sustainable development.

Original languageEnglish
Pages (from-to)1213-1233
Number of pages21
JournalProcess Safety and Environmental Protection
Publication statusPublished - Jul 2024


  • Industrial carbon emissions
  • Low-carbon production
  • Modeling and simulation
  • Multi-step forecasting

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • General Chemical Engineering
  • Safety, Risk, Reliability and Quality


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